Introduction

Calculating housing stress using 30/40 as in here: https://www.ahuri.edu.au/policy/ahuri-briefs/2016/3040-indicator

2016 Census - Counting Dwellings, Place of Enumeration (SA2)

Data Source: Census of Population and Housing, 2016, TableBuilder

Counting: Dwellings Location on Census Night

Variables:

library(tidyverse)
library(readxl)
library(sf)
library(tmap)
library(janitor)
library(scales)

Importing the raw data

##original files - 2016 census data - csv string - zeros suppressed
rentdata <- read_excel("sa2_data.xlsx", sheet = "sa2_hied_rntrd_2gmel_ zeros_sup", range = "A11:F49106") %>% 
  filter(GCCSA == "Greater Melbourne") 

mortdata <- read_excel("sa2_data.xlsx", sheet = "sa2_hied_mrerd_2gmel_ zeros_sup", range = "A11:F59708") %>% 
  filter(GCCSA == "Greater Melbourne")

Transforming the rent data

rent <- rentdata %>% 
  rename(sa2 = SA2,
         hhincwk = `HIED Equivalised Total Household Income (weekly)`,
         rentwk = `RNTRD Rent (weekly) Ranges`) %>% 
  mutate(hhincwk = as_factor(hhincwk), rentwk = as_factor(rentwk)) %>% 
  rename(hh = Count) %>% 
  select(-Counting)
rent
rent_dw <- rent %>%
  filter(hhincwk != 'Not applicable',
         rentwk != 'Not applicable') %>% 
  group_by(sa2) %>% 
  summarise(dw = sum(hh)) 
rent_dw #improve this by just getting the dwelling data from the datapacks and joining them on at the end -G32
rent_hh <- rent %>% 
  filter(!hhincwk %in% c('Nil income', 'Partial income stated', 'All incomes not stated', 'Not applicable'),
         !rentwk %in% c('Not stated', 'Not applicable' ))
rent_hh

Transforming the mortgage data

mort <- mortdata %>% 
  rename(sa2 = SA2,
         hhincwk = `HIED Equivalised Total Household Income (weekly)`,
         mortmth = `MRERD Mortgage Repayments (monthly) Ranges`) %>% 
  mutate(hhincwk = as_factor(hhincwk), mortmth = as_factor(mortmth)) %>% 
  rename(hh = Count) %>% 
  select(-Counting)
mort
mort_dw <- mort %>% 
  filter(hhincwk != 'Not applicable',
         mortmth != 'Not applicable') %>% 
  group_by(sa2) %>% 
  summarise(dw = sum(hh)) 
mort_dw #as noted above
mort_hh <- mort %>% 
  filter(!hhincwk %in% c('Nil income', 'Partial income stated', 'All incomes not stated', 'Not applicable'),
         !mortmth %in% c('Not stated', 'Not applicable'))
mort_hh

Midpoints for income

hhincwk_mid <- rentdata %>% 
  rename(sa2 = SA2,
         hhincwk = `HIED Equivalised Total Household Income (weekly)`) %>% 
  mutate(hhincwk = as_factor(hhincwk)) %>% 
  distinct(hhincwk) %>% 
  filter(!hhincwk %in% c('Nil income', 'Partial income stated', 'All incomes not stated', 'Not applicable', 'Total')) %>% 
  mutate(inc_low = c(1, 150, 300, 400, 500, 650, 800, 1000, 1250, 1500, 1750, 2000, 2500, 3000),
         inc_high = c(149, 299, 399, 499, 649, 799, 999, 1249, 1499, 1749, 1999, 2499, 2999, 3499),
         inc_mid = (inc_low+inc_high)/2) %>% 
  select(hhincwk, inc_mid)
hhincwk_mid

Filter to include only bottom 40 per cent of households by income

Equivalised disposable household income - P40 = $714 in 2015-16 for VIC - http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/6523.02015-16?OpenDocument

hhincwk_mid40 <- hhincwk_mid %>% 
  filter(inc_mid <= 714)
hhincwk_mid40

Midpoints for rent and mortgages

rentwk_mid <- rentdata %>% 
  rename(rentwk = `RNTRD Rent (weekly) Ranges`) %>% 
  distinct(rentwk) %>% 
  mutate(rentwk = as_factor(rentwk)) %>% 
  filter(!rentwk %in% c('Not stated', 'Not applicable', 'Total' )) %>% 
  mutate(rent_low = c(0, 1, 75, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 550, 650, 750, 850, 950),
         rent_high = c(0, 74, 99, 124, 149, 174, 199, 224, 249, 274, 299, 324, 349, 374, 399, 424, 449, 549, 649, 749, 849, 949, 1050),rent_mid = (rent_low + rent_high)/2) %>% 
  select(rentwk, rent_mid)
rentwk_mid
mortwk_mid <- mortdata %>% 
  rename(mortmth = `MRERD Mortgage Repayments (monthly) Ranges`) %>% 
  distinct(mortmth) %>% 
  mutate(mortmth = as_factor(mortmth)) %>% 
  filter(!mortmth %in% c('Not stated', 'Not applicable')) %>% 
  mutate(mort_low = c(0, 1, 150, 300, 450, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400, 2600, 3000, 4000, 5000),
         mortwk_low = mort_low/4,
         mort_high = c(0, 149, 299, 449, 599, 799, 999, 1199, 1399, 1599, 1799, 1999, 2199, 2399, 2599, 2999, 3999, 4999, 5999),
         mortwk_high = mort_high/4,
         mort_mid = (mort_low + mort_high)/2,
         mortwk_mid = (mortwk_low + mortwk_high)/2) %>% 
  select(mortmth, mort_mid, mortwk_mid)
mortwk_mid

Joining

#joining the data  ####
rent_join <- left_join(hhincwk_mid40, rent_hh)
## Joining, by = "hhincwk"
rent_join <- left_join(rent_join, rentwk_mid)
## Joining, by = "rentwk"
rent_join
mort_join <- left_join(hhincwk_mid40, mort_hh)
## Joining, by = "hhincwk"
mort_join <- left_join(mort_join, mortwk_mid)
## Joining, by = "mortmth"
mort_join

Stress

rentalstress <- rent_join %>% 
  mutate(prophousing = rent_mid/inc_mid,
         hstress = ifelse(prophousing <= 0.3, 0, hh)) %>% 
  group_by(sa2) %>% 
  summarise(lowinc_hh = sum(hh), hstress = sum(hstress))
rentalstress <- left_join(rentalstress, rent_dw, by = "sa2") %>% 
  select(sa2, dw, lowinc_hh, hstress) %>% 
  mutate(propstress = hstress/dw)
rentalstress
mortgagestress <- mort_join %>% 
  mutate(prophousing = mortwk_mid/inc_mid,
         hstress = ifelse(prophousing <= 0.3, 0, hh)) %>% 
  group_by(sa2) %>% 
  summarise(lowinc_hh = sum(hh), hstress = sum(hstress))
mortgagestress <- left_join(mortgagestress, mort_dw, by = "sa2") %>% 
  select(sa2, dw, lowinc_hh, hstress) %>% 
  mutate(propstress = hstress/dw)
mortgagestress 
housingstress_join <- full_join(rentalstress, mortgagestress)
## Joining, by = c("sa2", "dw", "lowinc_hh", "hstress", "propstress")
housingstress_sa2 <- housingstress_join %>% 
  group_by(sa2) %>% 
  summarise(dw = sum(dw), lowinc_hh = sum(lowinc_hh), hstress = sum(hstress)) %>% 
  mutate(prop_stress = hstress/dw) %>% 
  filter(!is.na(prop_stress))
housingstress_sa2
write_csv(housingstress_sa2, "tables_out/housingstress_sa2.csv")

What does it look like?

Most of the SA2s have fewer than 30 per cent of households in housing stress.

ggplot(housingstress_sa2, aes(x = prop_stress)) +
  geom_histogram(bins = 50)

The shapefile

gm_shp <- st_read("shp/SA2_2016_GMEL.shp") %>% 
  clean_names() %>% 
  select(sa2_name16) %>% 
  left_join(housingstress_sa2, by = c("sa2_name16" = "sa2")) %>% 
  mutate(prop_stress = round(prop_stress, 2))
## Reading layer `SA2_2016_GMEL' from data source 
##   `/home/williamlai/RScripts/housing_stress_2016/shp/SA2_2016_GMEL.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 309 features and 12 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 144.3336 ymin: -38.50299 xmax: 145.8784 ymax: -37.1751
## proj4string:   +proj=longlat +ellps=GRS80 +no_defs

The map

Concentrations in the growth areas.

tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(gm_shp) +
  tm_polygons("prop_stress")